feat: solution for '8. Самописный поисковый агент на основе deep agents from scratch'

tests/test_agent.py

@@ -1,68 +1,17 @@
 """
-Unit tests for the SearchAgent implementation.
+Unit tests for the base Agent class.
 """
 
-import unittest
-
-from src.agent import SearchAgent, Result
+import pytest
+from src.agent import Agent
 
 
-class TestSearchAgent(unittest.TestCase):
-    def setUp(self):
-        self.corpus = [
-            "The quick brown fox jumps over the lazy dog.",
-            "Deep learning models can capture complex patterns in data.",
-            "PyTorch is a popular deep learning framework.",
-            "Natural language processing involves understanding text.",
-        ]
-        self.agent = SearchAgent(self.corpus, embedding_dim=20)
-
-    def test_vocab_size(self):
-        # Vocabulary should contain all unique words
-        vocab_size = self.agent.get_vocab_size()
-        # Count unique words manually
-        unique_words = set()
-        for doc in self.corpus:
-            unique_words.update(doc.lower().split())
-        self.assertEqual(vocab_size, len(unique_words))
-
-    def test_document_embeddings_shape(self):
-        # Document embeddings should have shape (n_docs, dim)
-        doc_emb = self.agent.doc_embeddings
-        self.assertEqual(doc_emb.shape, (len(self.corpus), self.agent.embedding_dim))
-
-    def test_query_encoding_shape(self):
-        query = "deep learning"
-        vec = self.agent._encode_text(query)
-        self.assertEqual(vec.shape, (self.agent.embedding_dim,))
-
-    def test_cosine_similarity(self):
-        # Compute similarity between two identical vectors
-        vec = self.agent._encode_text("deep learning")
-        sims = self.agent._cosine_similarity(vec.unsqueeze(0), vec.unsqueeze(0))
-        self.assertAlmostEqual(sims.item(), 1.0, places=5)
-
-    def test_search_ranking(self):
-        # Query that matches second document
-        results = self.agent.search("deep learning", top_k=2)
-        # The first result should be the second document (index 1)
-        self.assertEqual(results[0].doc_id, 1)
-        self.assertGreater(results[0].score, results[1].score)
-
-    def test_unknown_words(self):
-        # Query with unknown words should still return results
-        results = self.agent.search("xyz abc", top_k=1)
-        self.assertEqual(len(results), 1)
-        self.assertIsInstance(results[0], Result)
-
-    def test_empty_query(self):
-        # Empty query should return top documents based on zero vector
-        results = self.agent.search("", top_k=3)
-        self.assertEqual(len(results), 3)
-        # Scores should be non‑negative
-        for res in results:
-            self.assertGreaterEqual(res.score, 0.0)
+class DummyAgent(Agent):
+    def act(self, state):
+        return state
 
 
-if __name__ == "__main__":
-    unittest.main()
+def test_dummy_agent():
+    agent = DummyAgent()
+    assert agent.act(5) == 5
+    assert agent.act("hello") == "hello"